4,085 research outputs found
Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices
Fall is one of the major health threats and obstacles to independent living
for elders, timely and reliable fall detection is crucial for mitigating the
effects of falls. In this paper, leveraging the fine-grained Channel State
Information (CSI) and multi-antenna setting in commodity WiFi devices, we
design and implement a real-time, non-intrusive, and low-cost indoor fall
detector, called Anti-Fall. For the first time, the CSI phase difference over
two antennas is identified as the salient feature to reliably segment the fall
and fall-like activities, both phase and amplitude information of CSI is then
exploited to accurately separate the fall from other fall-like activities.
Experimental results in two indoor scenarios demonstrate that Anti-Fall
consistently outperforms the state-of-the-art approach WiFall, with 10% higher
detection rate and 10% less false alarm rate on average.Comment: 13 pages,8 figures,corrected version, ICOST conferenc
Can smartwatches replace smartphones for posture tracking?
This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed
BodySpace: inferring body pose for natural control of a music player
We describe the BodySpace system, which uses inertial sensing and pattern recognition to allow the gestural control of a music player by placing the device at different parts of the body. We demonstrate a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based techniques can shape gestural interaction
The passive operating mode of the linear optical gesture sensor
The study evaluates the influence of natural light conditions on the
effectiveness of the linear optical gesture sensor, working in the presence of
ambient light only (passive mode). The orientations of the device in reference
to the light source were modified in order to verify the sensitivity of the
sensor. A criterion for the differentiation between two states: "possible
gesture" and "no gesture" was proposed. Additionally, different light
conditions and possible features were investigated, relevant for the decision
of switching between the passive and active modes of the device. The criterion
was evaluated based on the specificity and sensitivity analysis of the binary
ambient light condition classifier. The elaborated classifier predicts ambient
light conditions with the accuracy of 85.15%. Understanding the light
conditions, the hand pose can be detected. The achieved accuracy of the hand
poses classifier trained on the data obtained in the passive mode in favorable
light conditions was 98.76%. It was also shown that the passive operating mode
of the linear gesture sensor reduces the total energy consumption by 93.34%,
resulting in 0.132 mA. It was concluded that optical linear sensor could be
efficiently used in various lighting conditions.Comment: 10 pages, 14 figure
Gesture Based Home Automation for the Physically Disabled
Paralysis and motor-impairments can greatly reduce the autonomy and quality of life of a patient while presenting a major recurring cost in home-healthcare. Augmented with a non-invasive wearable sensor system and home-automation equipment, the patient can regain a level of autonomy at a fraction of the cost of home nurses. A system which utilizes sensor fusion, low-power digital components, and smartphone cellular capabilities can extend the usefulness of such a system to allow greater adaptivity for patients with various needs. This thesis develops such a system as a Bluetooth enabled glove device which communicates with a remote web server to control smart-devices within the home. The power consumption of the system is considered as a major component to allow the system to operate while requiring little maintenance, allowing for greater patient autonomy. The system is evaluated in terms of power consumption and accuracy to prove its viability as a home accessibility tool
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
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